The present invention relates to the field of neuron signal analysis, but is applicable to electromyograms and other applications where pulses from multiple sources need to be separated. More specifically, one embodiment of the present invention relates to solutions to the problem of separating a multi-unit, or composite, signal representing pulse trains from multiple neuron firings into single-unit signals each representing pulse trains from individual neurons.
Although problems of pulse train separation occur in a variety of fields, the problem occurs often in the tracking of neural signals. Because neurons interconnect in complex ways, the study of nervous system responses usually precludes the study of a single neuron. Often one or more electrical probes are placed in living tissue and neural electrical activity is captured. The signal is then processed in real-time or recorded for later processing. One signal per neuron is preferred, however such a signal is not always possible without disturbing the interconnections between the neurons. More typically, several pulse, or spike, trains from several neurons are detected by a probe, and processing of the probe signal requires separation of the multi-unit signal into single-unit signals.
Fortunately, spikes received by the probe from a particular neuron are similar in shape, while spikes received from different neurons are different in shape. This difference in shape allows spikes to be classified, or separated, into single-unit signals. As used herein, shape includes amplitude, so that two spikes which follow a similar line, but at different amplitudes, may be classified as spikes of different classes. Of course, due to noise and other factors, such as a close similarity in spike shape, separation is not perfect, and some spikes will be misclassified. Furthermore, separation is not trivial, since spikes from a single source might vary slightly in shape from time to time.
Before the present invention, several methods of signal separation were known in the art, however they provide insufficient user feedback, have a high cost, require complex computational requirements, or yield poor discrimination.
For example, U.S. Pat. No. 4,603,703, discloses a method for separation of spikes in a multi-unit signal which uses templates to classify spikes. According to that method, each spike is detected and compared against a set of sample spikes, or templates, and the spikes are classified according to which template, if any, matches the compared spike. The spikes matching a given template are then compiled into a component signal which is a reconstruction of the output of a single neuron.
Templates can be difficult to work with, however. If the separation is done digitally, considerable computation is required, and often the shape of a spike will fall between the shapes of two or more templates, so the spike will not be properly characterized. One problem with template matching systems is that the templates must be created before the separation begins, and they cannot be updated without interrupting the process of spike separation. The ability to update the templates while separation is on-going would allow a researcher to track the changes in shape of pulses from a particular neuron over time. Pulses from a neuron will change shape due to physiological effects or probe movement.
Another method of separation is discussed in an article by M. Abeles and M. Goldstein entitled "Multispike Train Analysis", Proc IEEE, vol 65, pp 762-773 (1977), which is incorporated herein for all purposes. Therein, a method of spike classification based on linear projection is disclosed. In the linear projection method, each spike is sampled and represented as a set of discrete values, in the form of a vector, corresponding to voltage samples at discrete moments in time. In a typical nervous system, 20-100 samples suffice to represent a spike, however less than 20 and more than 100 might be used depending on the bandwidth of the spikes and the frequency of spike occurrence. Thus, each spike is represented by a vector in an N-dimensional space, where N is the number of samples representing the spike.
To reduce the complexity of dealing with vectors in N dimensions, each vector is transformed into an alternate coordinate system which is defined by N orthonormal projection vectors. When a spike is projected onto each of the orthonormal vectors, the result is N inner products, or principal components (PC). These principal components define an N-dimensional vector in the alternate N-dimensional coordinate system, and the new vector equally well describes the original spike. The advantage to the alternate coordinate system is that the set of N orthonormal vectors can be selected such that the first two coordinates of a spike in the alternate coordinate system contain most of the variance from spike to spike. Thus, if the alternate coordinate system is properly selected, the difference vector between the original vector and a projection onto a plane, the P-plane, defined by the first two orthonormal vectors is minimized enough that, for the purposes of spike comparison, the original vectors are adequately represented by only two scalar values.
Only the first two orthonormal vectors need to be calculated, since the remaining vectors are never used. The first orthonormal vector is found by least squares fit of vectors representing a sample set of spikes, and the second orthonormal vector is found by a least squares fit of the differences of each vector in the sample set and its projection onto the first orthonormal vector, optimally leaving only a small error vector. Thus, the two orthonormal vectors defining the P-plane are chosen with reference to a sample set of spikes. Using a preset sample of spikes reduces the amount of computation required to generate the orthonormal vectors, but the error vectors will grow as the measured spikes vary statistically from the sample set.
Once the spikes are reduced to two-dimensional vectors, they can be displayed as points in the P-plane and viewed by a researcher on a planar display. Because spikes from a particular neuron have a common shape, the two vectors representing the spikes will be similar, and consequently, the corresponding points on the planar surface will be close together. Conversely, because spikes from different neurons have different shapes, the corresponding points in the P-plane for each of the spikes will be spaced far apart. Thus, points on the planar surface will tend to cluster, with each cluster indicating a different single-unit signal.
When the points are displayed on a visual display, such as a video graphics monitor, a researcher can easily identify the clusters of dots from single sources, or units, and indicate-the associations among the dots. One method of associating points is for the researcher to interactively draw an ellipse around a cluster of points on the display, and have a processor associate the spikes represented by the points within the ellipse as being from one single-unit signal.
However, such an interactive system is very complex, computationally demanding, and therefore expensive to implement, as each point must be compared with the curved edge of an ellipse to determine if the point is within the bounds of an ellipse. Such a comparison involves trigonometric calculations, and such calculations require considerable computing power in a digital computer. Furthermore, the manipulation of ellipses by a user attempting to bound particular set of points is cumbersome, as it is difficult to visually determine which points would lie inside an ellipse until the ellipse is drawn and positioned.
While systems have been built which separate multi-unit signals, they have heretofore been extremely costly and require complex hardware. In light of the above drawbacks of existing systems for separation of component single-unit signals from a multi-unit composite signal, an improved multi-unit analyzer is needed.